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and molecular data from prospective cohort studies. The main responsibilities will include managing and analysing complex epidemiological and molecular datasets, including high-dimensional omics data
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appropriate analytical techniques to quantify ion permeability. You will collaborate with colleagues, share knowledge and data and communicate your findings through reports, publications, seminars and
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learning for cardiovascular digital twins and AI-enabled precision treatment. The postholder will develop patient-specific models that integrate multimodal clinical, physiological, imaging and sensor data
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://jordibures.wixsite.com/buresgroup The successful candidate will have a PhD in chemistry, with a proven track record in catalysis, organic and organometallic chemistry, and mechanistic investigation. The position is full
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reinforcement learning (RL), Bayes adaptive RL, and planning with novel variants of Monte Carlo tree search. You should possess a relevant PhD/DPhil or be near completion together with relevant experience
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information. About You Essential criteria PhD qualified in relevant subject area (neuroscience or related discipline) In depth understanding of metal biology and metal-related disease pathology Proven knowledge
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, analysing and presenting data, contributing to manuscripts for publication and training, and supervising junior staff. You will also be required to contribute to scientific papers for publication and directly
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factors assessment). You should hold a relevant PhD/DPhil (or near completion) and have publications in medical image analysis or computer vision video analysis. Knowledge of ultrasound imaging is not a
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to define and evaluate AI biomarker models. You should hold a relevant technical PhD/DPhil (or near completion) in medical video analysis or computer vision with a video focus and ideally have prior
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, technicians, and PhD and project students. The researcher will develop landmarking approaches to capture key morphological parameters of trilobite eyes, such as area, shape, curvature, and predicted field